Interactive Visualization of US Monthly Birth Data (2000-2014) along with predection scatter plot & Lakes in Florida - Spatial Visulization

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Importing the required library.

library(tidyverse)
library(plotly)
library(sf) # For spatial data
library(ggplot2)

Loading the data from the data directory and reading it through the imported library.

birth_data_url <- "../data/us_births_00_14.csv"
births <- read.csv(birth_data_url, stringsAsFactors = FALSE)

filtering data to create plot

monthly_births <- births %>%
  group_by(year, month) %>%
  summarize(total_births = sum(births), .groups = 'drop') %>%
    mutate(date = as.Date(paste(year, month, "01", sep = "-")),
         month_name = format(date, "%B"))

Visualization 2 - Linear Model: Total Births Over Years

generating interactive plot from the filtered data

plot <- plot_ly(monthly_births, x = ~year, y = ~total_births, type = 'scatter', mode = 'lines+markers', text = ~paste("Year:", year, "<br>Month:", month_name, "<br>Total Births:", total_births), 
                hoverinfo = 'text') %>%
  layout(
    title = "Total Monthly Births in the US (2000-2014)",
    xaxis = list(title = "Year"),
    yaxis = list(title = "Total Births")
  )
plot

Visualization 2 - Linear model: Predicting total births based on year

Linear model: Predicting total births based on year

model <- lm(total_births ~ year, data = monthly_births)

Summary of the model

summary(model)

Call:
lm(formula = total_births ~ year, data = monthly_births)

Residuals:
   Min     1Q Median     3Q    Max 
-44575 -12125     57  13426  50872 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  2361462     624179   3.783 0.000211 ***
year           -1004        311  -3.230 0.001475 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 18030 on 178 degrees of freedom
Multiple R-squared:  0.05536,   Adjusted R-squared:  0.05005 
F-statistic: 10.43 on 1 and 178 DF,  p-value: 0.001475

Plot the model with ggplot2

ggplot(monthly_births, aes(x = year, y = total_births)) +
  geom_point() +
  geom_smooth(method = "lm", col = "#532D8E") +
  labs(title = "Linear Model: Total Births Over Years", x = "Year", y = "Total Births") +
  theme_minimal()

`geom_smooth()` using formula = 'y ~ x'

Visualization 3 - Lakes in Florida

Reading the shape file from the data directory

lakes <- st_read("../data/Florida_Lakes/Florida_Lakes.shp")

Reading layer `Florida_Lakes' from data source 
  `C:\Users\desdiv10\Documents\Assignments\Data Visualization\workspace\dataviz_final_project\data\Florida_Lakes\Florida_Lakes.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 4243 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -87.42774 ymin: 25.02625 xmax: -80.03097 ymax: 31.00254
Geodetic CRS:  WGS 84

mapping lakes to the florida map using red color

ggplot() +
  geom_sf(data = lakes, fill = "red", color = "black") +
  ggtitle("Lakes in Florida") +
  theme_minimal()